The Impact of Stigmatizing Language in EHR Notes on AI Performance and Fairness

Today, there is significant interest in using electronic health record data to generate new clinical insights for diagnosis and treatment decisions. However, there are concerns that such data may be biased and result in accentuating racial disparities. We study how clinician biases reflected in EHR notes affect the performance and fairness of artificial intelligence models in the context of mortality prediction for intensive care unit patients. We apply a Transformer-based deep learning model and explainable AI techniques to quantify negative impacts on performance and fairness. Our findings demonstrate that stigmatizing language written by clinicians adversely affects AI performance, particularly so for black patients, highlighting SL as a source of racial disparity in AI model development. As an effective mitigation approach, removing SL from EHR notes can significantly improve AI performance and fairness. This study provides actionable insights for responsible AI development and contributes to understanding clinician EHR note writing

Probabilistic Fair Clustering

In clustering problems, a central decision-maker is given a complete metric graph over vertices and must provide a clustering of vertices that minimizes some objective function. In fair clustering problems, vertices are endowed with a color (e.g., membership in a group), and the features of a valid clustering might also include the representation of colors in that clustering. Prior work in fair clustering assumes complete knowledge of group membership. In this paper, we generalize prior work by assuming imperfect knowledge of group membership through probabilistic assignments. We present clustering algorithms in this more general setting with approximation ratio guarantees. We also address the problem of "metric membership", where different groups have a notion of order and distance. Experiments are conducted using our proposed algorithms as well as baselines to validate our approach and also surface nuanced concerns when group membership is not known deterministically

Explaining classification performance and bias via network structure and sampling technique

Social networks are very important carriers of information. For instance, the political leaning of our friends can serve as a proxy to identify our own political preferences. This explanatory power is leveraged in many scenarios ranging from business decision-making to scientific research to infer missing attributes using machine learning. However, factors affecting the performance and the direction of bias of these algorithms are not well understood. To this end, we systematically study how structural properties of the network and the training sample influence the results of collective classification. Our main findings show that (i) mean classification performance can empirically and analytically be predicted by structural properties such as homophily, class balance, edge density and sample size, (ii) small training samples are enough for heterophilic networks to achieve high and unbiased classification performance, even with imperfect model estimates, (iii) homophilic networks are more prone to bias issues and low performance when group size differences increase, (iv) when sampling budgets are small, partial crawls achieve the most accurate model estimates, and degree sampling achieves the highest overall performance. Our findings help practitioners to better understand and evaluate their results when sampling budgets are small or when no ground-truth is available

People Talking and AI Listening: How Stigmatizing Language in EHR Notes Affect AI Performance

Electronic health records (EHRs) serve as an essential data source for the envisioned artificial intelligence (AI)-driven transformation in healthcare. However, clinician biases reflected in EHR notes can lead to AI models inheriting and amplifying these biases, perpetuating health disparities. This study investigates the impact of stigmatizing language (SL) in EHR notes on mortality prediction using a Transformer-based deep learning model and explainable AI (XAI) techniques. Our findings demonstrate that SL written by clinicians adversely affects AI performance, particularly so for black patients, highlighting SL as a source of racial disparity in AI model development. To explore an operationally efficient way to mitigate SL's impact, we investigate patterns in the generation of SL through a clinicians' collaborative network, identifying central clinicians as having a stronger impact on racial disparity in the AI model. We find that removing SL written by central clinicians is a more efficient bias reduction strategy than eliminating all SL in the entire corpus of data. This study provides actionable insights for responsible AI development and contributes to understanding clinician behavior and EHR note writing in healthcare.Comment: 54 pages, 9 figure